Abstract
BackgroundA number of alignment tools have been developed to align sequencing reads to the human reference genome. The scale of information from next-generation sequencing (NGS) experiments, however, is increasing rapidly. Recent studies based on NGS technology have routinely produced exome or whole-genome sequences from several hundreds or thousands of samples. To accommodate the increasing need of analyzing very large NGS data sets, it is necessary to develop faster, more sensitive and accurate mapping tools.ResultsHIA uses two indices, a hash table index and a suffix array index. The hash table performs direct lookup of a q-gram, and the suffix array performs very fast lookup of variable-length strings by exploiting binary search. We observed that combining hash table and suffix array (hybrid index) is much faster than the suffix array method for finding a substring in the reference sequence. Here, we defined the matching region (MR) is a longest common substring between a reference and a read. And, we also defined the candidate alignment regions (CARs) as a list of MRs that is close to each other. The hybrid index is used to find candidate alignment regions (CARs) between a reference and a read. We found that aligning only the unmatched regions in the CAR is much faster than aligning the whole CAR. In benchmark analysis, HIA outperformed in mapping speed compared with the other aligners, without significant loss of mapping accuracy.ConclusionsOur experiments show that the hybrid of hash table and suffix array is useful in terms of speed for mapping NGS sequencing reads to the human reference genome sequence. In conclusion, our tool is appropriate for aligning massive data sets generated by NGS sequencing.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-015-0062-4) contains supplementary material, which is available to authorized users.
Highlights
A number of alignment tools have been developed to align sequencing reads to the human reference genome
Our experiment showed that the hash table index can decrease considerably the searching time
Evaluation data sets and evaluation measurements We made six datasets from the GRCH37 build of the human genome, using Mason [19]. Two of these are unpaired Illumina-like datasets, consisting respectively of one million 100 bp reads and one million 150 bp reads, which Mason simulated with parameters ‘illumina -hn 2 -sq -n 100 -N 1000000’ and ‘illumina -hn 2 -sq -n 150 -N 1000000’
Summary
A number of alignment tools have been developed to align sequencing reads to the human reference genome. Recent studies based on NGS technology have routinely produced exome or whole-genome sequences from several hundreds or thousands of samples. Recent studies based on next-generation sequencing (NGS) technology have produced hundreds or thousands of exome or whole genome sequences with decreasing cost of NGS experiments [1]. To keep pace with developing NGS technologies, many alignment tools have been developed for both short and long reads. These tools include SSAHA2 [3], BWA [4, 5], AGILE [6], SOAP2 [7], Bowtie2 [8], SeqAlto [9] and others. Most BWT-based alignment tools use the full-text minute index [13], which is memory-efficient and similar to the suffix tree. With respect to matching time, the suffix tree is efficient for
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